1. Field of the Invention
The present disclosure relates to an apparatus for forecasting water demand of a waste system using an automation system.
2. Background of the Invention
Accurately estimating an amount of water demand in a water treatment system installed in a water distribution reservoir (or a service reservoir), a pure water reservoir, and the like, is a primary factor considered to be the most important in water operation and management, and a water management plan is established according to the estimation results.
In a related art water demand estimating technique of a water treatment system, daily water demand is estimated by simply linearizing daily water demand in a time series manner.
FIG. 1 is a flow chart illustrating an operation for estimating water demand according to the related art.
Referring to FIG. 1, the related art water demand estimating apparatus accesses a database of a host server to collect daily water demand history data for estimating daily water demand of waterworks (or a water supply service) in step S11.
With the collected daily water demand history data, the water demand estimating apparatus may perform an initial process in which the water demand estimating apparatus may search for error data, missing data, and the like, and corrects the searched error data and missing data and convert the corrected data into daily water demand estimate data such that the error data and missing data can be used again as previous average values in step S12.
The water demand estimating apparatus may apply the initially processed daily water demand history data to a certain preset model (a neural network model, a time series model, and the like) to perform a learning data process in step S13.
The water demand estimating apparatus may estimate daily water demand in real time by using the result obtained by performing the learning data process in step S14.
As described above, in the related art daily water demand estimation, the water demand in a time series regarding estimation of demand of water supplied through a water pipe network is not linear and complicated, but, since daily water demand is estimated by using a unit time series such as an artificial intelligence scheme of a neural network or a statistical scheme of a time series module, accuracy of prediction of daily water demand is low.
Thus, since water demand is estimated without consideration of various problems of the on-site characteristics and data acquirement, the estimated water demand may be significantly different from substantial water demand.